Commercial enterprises compete for customers by promising, among other things, low prices and fast delivery. Successful competition often requires careful monitoring of profit margins and deadlines. One key to success in this environment is a system that provides accurate and timely business information. Financial data and other information that indicates the state of the corporation can no longer be examined only on a periodic basis, but rather must be continually monitored. Businesses rely on their latest performance information to support strategic planning and decision making, so any businesses without a system for providing accurate and timely business information would be at a huge disadvantage relative to their competitors.
Accordingly, most businesses track at least their financial data in a computerized financial reporting system that can generate reports on demand. Many large entities have reporting systems that process large numbers of complex transactions which may be occurring at many locations around the world.
Businesses often wish to use such computerized data to forecast some outcome (e.g., end-of-quarter revenue, end-of-month inventory, or end-of-year overhead costs) or to monitor the probability of achieving some goal to support current business decisions. This task may be quite challenging. A large enterprise's ongoing transactions are complex and difficult to model. One alternative to constructing transaction-based models is to employ stochastic modeling techniques for forecasting. Many stochastic modeling approaches are based on time-series models. Autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) models inherently assume that the data is stationary (in the statistical sense of having a fixed average and standard deviation), which makes them unsuitable for many real world applications. The autoregressive integrated moving average (ARIMA) model weakens the requirement for stationarity, requiring only that the data have a stationary derivative (i.e., a differenced time series that can be integrated to recover the original time series).
Real world data rarely follows any neat or closed-form stochastic models such as those given by the foregoing time-series models. Though a good correspondence can often be achieved with existing data that is used for training the model, the future predictions made by such models are inadequate for many applications, and degrade when model complexity is increased. An alternative approach to closed-form stochastic models would be desirable for forecasting in the business environment.